• Design, build, and maintain robust ETL/ELT data pipelines for batch and streaming workloads. • Implement data ingestion and transformation workflows using Apache Airflow, Apache NiFi, Apache Spark, and Kafka. • Integrate data from multiple sources including REST APIs, files, relational databases, message queues, and external SaaS platforms. • Optimize pipelines for performance, scalability, reliability, and cost efficiency. • Develop and operate a centralized data integration hub that supports multiple upstream and downstream systems. • Build reusable, modular integration components and frameworks. • Ensure high availability, fault tolerance, and observability of data workflows. • Design and manage data warehouses, data lakes, and operational data stores using PostgreSQL and related technologies. • Implement appropriate data modeling strategies for analytical and operational use cases. • Manage schema evolution, metadata, and versioning. • Implement data validation, monitoring, and reconciliation mechanisms to ensure data accuracy and completeness. • Enforce data security best practices, access controls, and compliance with internal governance policies. • Establish logging, alerting, and auditability across pipelines. • Automate data workflows, deployments, and operational processes to support scale and reliability. • Monitor pipelines proactively and troubleshoot production issues. • Improve CI/CD practices for data engineering workflows. • Work closely with data scientists, analysts, backend engineers, and business stakeholders to understand data requirements. • Translate business needs into technical data solutions.

• Design and develop conceptual, logical, and physical data models for various domains • Lead the development of data modeling standards, best practices, and guidelines • Develop end-to-end solution architectures for data-driven and AI-focused applications • Mentor and guide junior data modelers • Design data models to support enterprise and operational reporting • Collaborate with data scientists to develop features for machine learning models • Optimize data models for performance and scalability • Ensure data models comply with data governance policies and standards • Identify and define data quality checks and validation processes • Implement data quality monitoring and reporting mechanisms

• Análisis, diseño y desarrollo de soluciones de ingeniería de datos en entornos cloud. • Construcción y mantenimiento de pipelines de datos. • Tratamiento, integración y análisis de grandes volúmenes de información. • Optimización de procesos de datos en plataformas Microsoft Azure. • Data engineering y data analysis.

• Define the target-state Azure data architecture (ingestion, orchestration, storage zones, serving patterns), security/networking boundaries, cost/perf tradeoffs, and promotion strategy (Dev→Test→Prod). • Implement robust ELT/ETL with ADF/Synapse Pipelines (parameters, reusable templates, CI/CD). • Hands-on in T-SQL and Python/PySpark for transformations, utilities, and tests. • Physical/semantic modeling, partitioning, columnstore strategies, statistics management, query plan analysis, index design, concurrency & transaction isolation, workload management. • SLA/SLO definitions, Azure Monitor / Log Analytics / App Insights dashboards and alerts; error handling, retries/backoff, idempotency, CDC and schema drift strategies. • RBAC, Key Vault, managed identities, private endpoints/VNet, data masking patterns; document data contracts and access patterns. • Code reviews, PR discipline, mentoring, and crisp documentation/runbooks for client handoff.